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I expect hybrid deep learning and symbolic AI systems to be highly relevant. My background which is what I base the following opinions on: I spent the 1980s mos
by mark_l_watson 8y ago
I expect hybrid deep learning and symbolic AI systems to be highly relevant. My background which is what I base the following opinions on: I spent the 1980s mostly doing symbolic AI except for 2 years of neural networks (wrote first version of SAIC Ansim neural network library, supplied the code for a bomb detector we did for the FAA, on DARPA neural net advisory panel for a year). For the last 6 years, just about 100% all-in working with deep learning.
My strong hunch is that deep learning results will continue to be very impressive and that with improved tooling basic applications of deep learning will become fairly much automated, so the millions of people training to be deep learning practitioners may have short careers; there will always be room for the top researchers but I expect model architecture search, even faster hardware, and AIs to build models (AdaNet, etc.) will replace what is now a lot of manual effort.
For hybrid systems, I have implemented enough code in Racket Scheme to run pre-trained Keras dense models (code in my public github repos) and for a new side project I am using BERT, etc. pre-trained models wrapped with a REST interface, and my application code in Common Lisp has wrappers to make the REST calls so I am treating each deep learning model as a callable function.
- p1esk 8y agomillions of people training to be deep learning practitioners may have short careers Have database admins disappeared? How about front end devs?
- ElFitz 8y agoI don't know if database admins have disappeared. But we have never needed one to take care of our DynamoDB tables and our "serverless" Aurora databases. Even though I'm pretty sure AWS needs a lot of them; although not one (or more) for each and every single one of their customers.
- oneplane 8y agoI sure your queries are horrible :p DBA's are generally not just scoped to keeping the RDBMS alive.
- ElFitz 8y agoHaha ^^ My SQL skills are indeed quite limited. But most of my stuff relies on DynamoDB, mostly in a strictly key-value fashion. Still; queries are a lot less to learn and much easier to fix when I do them wrong then also queries + taking care of (maintaining, scaling, fixing, backing up,...) the database itself.
- Findeton 8y agoWhat do you think about conceptual AI? AI models that are able to create, test, modify ideas/concepts on the fly. We need a breakthrough...
- usrusr 8y ago(not GP, just an interested passer-by) Based on my limited contact with AI during the aughts' semantic web/description logic symbolic heyday (we were exploring ways in which multiple communicating knowledge bases might resolve conflicting information): symbolics with uncertainty is too hard, maybe in a very far future. When ML and symbolics are successfully put together, I expect the symbolics to focus on what they do best: ignore uncertainty and change, leave all that to the ML part. For example, when you do the "obvious thing" and run symbolic reasoning on top of classifications supplied by ML (which is maybe a naive approach not working out at all, I have no idea), you would feed back corrective training updates into the classification layer instead of softening the concepts when the outcome of reasoning is not satisfactory. Imaginary toy example: if your rules state that cars always stop at stop signs, but the observed reality is that this hardly ever happens, this first iteration of ML-fed symbolics would not adopt by adjusting the rules to "cars carefully approach stop signs, but don't actually stop", it would eventually adopt by classifying the red octagonal shape as a yield sign, keeping the rules for stop signs as is (but never seeing any).